Are you also learning about AI/Machine Learning/Deep Learning? As I keep learning, I’ll share the resources I’ve used on this page. If you have any recommendations for learning resources I should check out, please contact me!
This book is available online in its entirety. It is a good place to start for those who want to understand all the mathematical underpinnings of machine learning and deep learning. The first few chapters of the book cover topics like linear algebra and “machine learning basics.”
If you’re struggling to get to the first few chapters of Goodfellow’s Deep Learning book, it might be helpful to watch some videos from the Khan Academy on basic topics like “Introduction to Matrices” and “Matrix Multiplication.” I worked through the first chapters of Goodfellow’s book by pausing whenever I did not understand something and watching a Khan Academy video on that concept.
This YouTube channel features tons of videos by computer scientist Andrew Ng. This is an excellent resource for anyone who wants to learn about deep learning but feels a bit intimated. Ng takes you through all the concepts you need to know at a very slow pace, and does not presuppose any prior knowledge.
This lecture series gives a comprehensive introduction to deep learning for computer vision. It doesn’t presuppose a lot of knowledge about deep learning or mathematics, and is therefore suitable to those who are new to the field but would still like a deep dive into computer vision. The pace of the lectures is quite fast, but luckily you can always pause and replay certain sections. The course is led by Fei-Fei Li, a big name in the field of computer vision.
Introductory books to machine learning often contrast machine learning and deep learning with “rule-based” or “expert systems.” If you want a super accessible introduction to expert systems, watch this lecture by Patrick Winston at MIT.
Codecademy is a website that offers free and paid plans for online coding courses. The courses consist of short lessons with many practical coding exercises. I took the Python 2 course, as someone who’d never coded before, and it was a very easy way to get started.